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Accelerating the Development of Personalized Cancer Immunotherapy by Integrating Molecular Patients' Profiles with
Zvia Agur1, Moran Elishmereni1, Urszula Foryś2
1Institute for Medical Biomathematics (IMBM), Bene Ataroth, Israel.
Clinical Pharmacology and Therapeutics
|June 15, 2020
Summary
Precision medicine tailors treatments using patient data but faces limits. Integrating dynamic modeling with static profiling can advance personalized cancer immunotherapy.
Area of Science:
- Translational Medicine
- Computational Biology
- Oncology
Background:
- The paradigm shift from "one-size-fits-all" to precision medicine involves tailoring treatments to individual patient characteristics.
- Precision medicine utilizes advanced statistical methods to link static patient profiling (genomic, proteomic) with clinical outcomes.
- Current precision medicine technologies, especially in oncology and cancer immunotherapy, are nearing their limits due to challenges in integrating diverse patient data.
Purpose of the Study:
- To review the evolution, achievements, and limitations of precision medicine.
- To explore dynamic modeling approaches for treatment personalization based on patient-disease-drug system interactions.
- To propose a roadmap for integrating static and dynamic approaches to enhance cancer immunotherapy.
Main Methods:
- Review of existing literature on precision medicine, static patient profiling, and mathematical modeling of dynamic systems.
- Evaluation of algorithms for predicting individual patient disease dynamics under immunotherapeutic drugs.
- Conceptual framework for amalgamating static and dynamic approaches in precision medicine.
Main Results:
- Precision medicine has shown significant advances, particularly in oncology, but struggles with data integration.
- Dynamic modeling offers a complementary approach by analyzing patient-disease-drug interactions, with developing predictive algorithms.
- The integration of static and dynamic approaches holds substantial potential for improving personalized cancer immunotherapy.
Conclusions:
- Amalgamating static patient profiling with dynamic modeling is crucial for maximizing the potential of precision medicine in cancer immunotherapy.
- Further applicability and validation of dynamic approaches are needed.
- Collaboration between clinicians, pharmacologists, and computational biologists is essential for advancing personalized cancer treatment.
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